Timezone: »
Poster
The balancing principle for parameter choice in distance-regularized domain adaptation
Werner Zellinger · Natalia Shepeleva · Marius-Constantin Dinu · Hamid Eghbal-zadeh · Hoan Duc Nguyen · Bernhard Nessler · Sergei Pereverzyev · Bernhard A. Moser
We address the unsolved algorithm design problem of choosing a justified regularization parameter in unsupervised domain adaptation. This problem is intriguing as no labels are available in the target domain. Our approach starts with the observation that the widely-used method of minimizing the source error, penalized by a distance measure between source and target feature representations, shares characteristics with regularized ill-posed inverse problems. Regularization parameters in inverse problems are optimally chosen by the fundamental principle of balancing approximation and sampling errors. We use this principle to balance learning errors and domain distance in a target error bound. As a result, we obtain a theoretically justified rule for the choice of the regularization parameter. In contrast to the state of the art, our approach allows source and target distributions with disjoint supports. An empirical comparative study on benchmark datasets underpins the performance of our approach.
Author Information
Werner Zellinger (Software Competence Center Hagenberg GmbH)
Natalia Shepeleva (Johannes Kepler University Linz)
Marius-Constantin Dinu (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Dynatrace Research)
Hamid Eghbal-zadeh (LIT AI Lab & Institute of Computational Perception, Johannes Kepler University of Linz)
Hoan Duc Nguyen (The Johann Radon Institute for Computational and Applied Mathematics)
Bernhard Nessler (LIT AI Lab, University Linz)
Sergei Pereverzyev (The Johann Radon Institute for Computational and Applied Mathematics (RICAM))
Bernhard A. Moser
More from the Same Authors
-
2021 : Understanding the Effects of Dataset Composition on Offline Reinforcement Learning »
Kajetan Schweighofer · Markus Hofmarcher · Marius-Constantin Dinu · Philipp Renz · Angela Bitto · Vihang Patil · Sepp Hochreiter -
2021 : Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning »
Kajetan Schweighofer · Markus Hofmarcher · Marius-Constantin Dinu · Philipp Renz · Angela Bitto · Vihang Patil · Sepp Hochreiter -
2021 : Understanding the Effects of Dataset Composition on Offline Reinforcement Learning »
Kajetan Schweighofer · Markus Hofmarcher · Marius-Constantin Dinu · Angela Bitto · Philipp Renz · Vihang Patil · Sepp Hochreiter -
2021 : Understanding the Effects of Dataset Composition on Offline Reinforcement Learning »
Kajetan Schweighofer · Markus Hofmarcher · Marius-Constantin Dinu · Angela Bitto · Philipp Renz · Vihang Patil · Sepp Hochreiter -
2019 : Poster and Coffee Break 2 »
Karol Hausman · Kefan Dong · Ken Goldberg · Lihong Li · Lin Yang · Lingxiao Wang · Lior Shani · Liwei Wang · Loren Amdahl-Culleton · Lucas Cassano · Marc Dymetman · Marc Bellemare · Marcin Tomczak · Margarita Castro · Marius Kloft · Marius-Constantin Dinu · Markus Holzleitner · Martha White · Mengdi Wang · Michael Jordan · Mihailo Jovanovic · Ming Yu · Minshuo Chen · Moonkyung Ryu · Muhammad Zaheer · Naman Agarwal · Nan Jiang · Niao He · Nikolaus Yasui · Nikos Karampatziakis · Nino Vieillard · Ofir Nachum · Olivier Pietquin · Ozan Sener · Pan Xu · Parameswaran Kamalaruban · Paul Mineiro · Paul Rolland · Philip Amortila · Pierre-Luc Bacon · Prakash Panangaden · Qi Cai · Qiang Liu · Quanquan Gu · Raihan Seraj · Richard Sutton · Rick Valenzano · Robert Dadashi · Rodrigo Toro Icarte · Roshan Shariff · Roy Fox · Ruosong Wang · Saeed Ghadimi · Samuel Sokota · Sean Sinclair · Sepp Hochreiter · Sergey Levine · Sergio Valcarcel Macua · Sham Kakade · Shangtong Zhang · Sheila McIlraith · Shie Mannor · Shimon Whiteson · Shuai Li · Shuang Qiu · Wai Lok Li · Siddhartha Banerjee · Sitao Luan · Tamer Basar · Thinh Doan · Tianhe Yu · Tianyi Liu · Tom Zahavy · Toryn Klassen · Tuo Zhao · Vicenç Gómez · Vincent Liu · Volkan Cevher · Wesley Suttle · Xiao-Wen Chang · Xiaohan Wei · Xiaotong Liu · Xingguo Li · Xinyi Chen · Xingyou Song · Yao Liu · YiDing Jiang · Yihao Feng · Yilun Du · Yinlam Chow · Yinyu Ye · Yishay Mansour · · Yonathan Efroni · Yongxin Chen · Yuanhao Wang · Bo Dai · Chen-Yu Wei · Harsh Shrivastava · Hongyang Zhang · Qinqing Zheng · SIDDHARTHA SATPATHI · Xueqing Liu · Andreu Vall -
2018 : Coffee break + posters 2 »
Jan Kremer · Erik McDermott · Brandon Carter · Albert Zeyer · Andreas Krug · Paul Pu Liang · Katherine Lee · Dominika Basaj · Abelino Jimenez · Lisa Fan · Gautam Bhattacharya · Tzeviya S Fuchs · David Gifford · Loren Lugosch · Orhan Firat · Benjamin Baer · JAHANGIR ALAM · Jamin Shin · Mirco Ravanelli · Paul Smolensky · Zining Zhu · Hamid Eghbal-zadeh · Skyler Seto · Imran Sheikh · Joao Felipe Santos · Yonatan Belinkov · Nadir Durrani · Oiwi Parker Jones · Shuai Tang · André Merboldt · Titouan Parcollet · Wei-Ning Hsu · Krishna Pillutla · Ehsan Hosseini-Asl · Monica Dinculescu · Alexander Amini · Ying Zhang · Taoli Cheng · Alain Tapp -
2018 : Hamid Eghbal-zadeh, "Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning" »
Hamid Eghbal-zadeh -
2018 : Coffee break + posters 1 »
Samuel Myer · Wei-Ning Hsu · Jialu Li · Monica Dinculescu · Lea Schönherr · Ehsan Hosseini-Asl · Skyler Seto · Oiwi Parker Jones · Imran Sheikh · Thomas Manzini · Yonatan Belinkov · Nadir Durrani · Alexander Amini · Johanna Hansen · Gabi Shalev · Jamin Shin · Paul Smolensky · Lisa Fan · Zining Zhu · Hamid Eghbal-zadeh · Benjamin Baer · Abelino Jimenez · Joao Felipe Santos · Jan Kremer · Erik McDermott · Andreas Krug · Tzeviya S Fuchs · Shuai Tang · Brandon Carter · David Gifford · Albert Zeyer · André Merboldt · Krishna Pillutla · Katherine Lee · Titouan Parcollet · Orhan Firat · Gautam Bhattacharya · JAHANGIR ALAM · Mirco Ravanelli -
2017 Poster: GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium »
Martin Heusel · Hubert Ramsauer · Thomas Unterthiner · Bernhard Nessler · Sepp Hochreiter